| C04-1192 | Standard ( GS ) against which the | WSD algorithm | was evaluated . Additionally |
| C04-1133 | feature sets used in the supervised | WSD algorithms | at best use only minimal information |
| A00-3007 | evaluation in this study , i.e. the | WSD algorithm | is tested independent of the |
| A00-3007 | can therefore conclude that our | WSD algorithm | is better than no disambiguation |
| D14-1065 | of the performance of existing | WSD algorithms | for a multilingual context is |
| A97-2010 | words , previous corpus-based | WSD algorithms | learn to disambiguate a polysemous |
| D09-1048 | and then running two different | WSD algorithms | . The accuracy values of approximately |
| D14-1110 | propose two simple and efficient | WSD algorithms | to obtain more relevant occurrences |
| D09-1029 | Wikipedia page Building . 4 The | WSD Algorithm | Gliozzo et al. ( 2005 ) proposed |
| D14-1065 | Section 2.1 , BabelNet provides | WSD algorithms | for multilingual corpora . More |
| A97-2010 | which explains why most previous | WSD algorithms | only deal with a dozen of polysemous |
| D14-1042 | enhance performance of standard | WSD algorithms | . A comprehensive overview of |
| E06-1018 | pseudoword-based evaluation method for | WSD algorithms | . The idea is to take two arbitrarily |
| E06-3009 | defined a priori . Second , classic | WSD algorithms | take training instances of one |
| D14-1110 | to evaluate our knowledge-based | WSD algorithm | based on the sense vectors . |
| E06-1018 | are viewed as one set and the | WSD algorithm | is then supposed to sort them |
| A00-3007 | context ( ties are allowed ) . Our | WSD algorithm | was also fed with the identical |
| A00-3007 | training methods , we have adopted a | WSD algorithm | which avoids the necessity for |
| A00-3007 | determine the sense of a word , a | WSD algorithm | typically uses the context of |
| A97-2010 | polysemous words . We demonstrate a new | WSD algorithm | that relies on a different intuition |
